Tutorial-12:Forward propagation in neural networks

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Forward propagation is the process by which input data flows through a neural network to generate an output. It involves a sequence of mathematical computations, moving layer by layer, starting from the input layer and progressing through hidden layers to the output layer. The goal of forward propagation is to compute the network's prediction for a given input.

Key Steps in Forward Propagation:
1.Input Layer:The input data, represented as a vector, is passed into the network.
2.Linear Transformation:At each layer, the input is multiplied by a set of weights (W) and added to a bias term (b) to produce a weighted sum:
Z=W⋅X+b
Here:X is the input from the previous layer.
𝑊,b are the trainable parameters of the current layer.
3.Activation Function:
The weighted sum (Z) is passed through an activation function (e.g., ReLU, sigmoid, or tanh) to introduce non-linearity. The output is:
A=Activation(Z)
4.Propagation Through Layers:
The activated output (A) becomes the input for the next layer, and the process repeats for all hidden layers.
5.Output Layer:
The final layer computes the output, often applying an activation function suitable for the task, such as:
Softmax for classification problems.
Linear activation for regression problems.
6.Output:The final result is the network's prediction for the input data.
Purpose of Forward Propagation:
To compute predictions for a given set of inputs.
It is used during both training (to compute the loss) and inference (to make predictions).
Forward propagation is computationally efficient because it involves a series of matrix multiplications and activation function evaluations. It sets the stage for backpropagation, where the network adjusts its weights to improve predictions.

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